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docs/Makefile
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.PHONY: help Makefile
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Capsule Network\n",
"================\n",
"**Author**: `Jinjing Zhou`\n",
"\n",
"This tutorial explains how to use DGL library and its language to implement the [capsule network](http://arxiv.org/abs/1710.09829) proposed by Geoffrey Hinton and his team. The algorithm aims to provide a better alternative to current neural network structures. By using DGL library, users can implement the algorithm in a more intuitive way."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model Overview\n",
"\n",
"### Introduction\n",
"Capsule Network were first introduced in 2011 by Geoffrey Hinton, et al., in a paper called [Transforming Autoencoders](https://www.cs.toronto.edu/~fritz/absps/transauto6.pdf), but it was only a few months ago, in November 2017, that Sara Sabour, Nicholas Frosst, and Geoffrey Hinton published a paper called Dynamic Routing between Capsules, where they introduced a CapsNet architecture that reached state-of-the-art performance on MNIST.\n",
"\n",
"### What's a capsule?\n",
"> A capsule is a group of neurons whose activity vector represents the instantiation parameters of a specific type of entity such as an object or an object part. \n",
"\n",
"Generally Speaking, the idea of capsule is to encode all the information about the features into a vector form, by substituting scalars in traditional neural network with vectors. And use the norm of the vector to represents the meaning of original scalars. \n",
"![figure_1](./capsule_f1.png)\n",
"\n",
"### Dynamic Routing Algorithm\n",
"Due to the different structure of network, capsules network has different operations to calculate results. This figure shows the comparison, drawn by [Max Pechyonkin](https://medium.com/ai%C2%B3-theory-practice-business/understanding-hintons-capsule-networks-part-ii-how-capsules-work-153b6ade9f66O). \n",
"<img src=\"./capsule_f2.png\" style=\"height:250px;\"/><br/>\n",
"\n",
"The key idea is that the output of each capsule is the sum of weighted input vectors. We will go into details in the later section with code implementations.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model Implementations\n",
"\n",
"### 1. Consider capsule routing as a graph structure\n",
"\n",
"We can consider each capsule as a node in a graph, and connect all the nodes between layers.\n",
"<img src=\"./capsule_f3.png\" style=\"height:200px;\"/>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def construct_graph(self):\n",
" g = dgl.DGLGraph()\n",
" g.add_nodes(self.input_capsule_num + self.output_capsule_num)\n",
" input_nodes = list(range(self.input_capsule_num))\n",
" output_nodes = list(range(self.input_capsule_num, self.input_capsule_num + self.output_capsule_num))\n",
" u, v = [], []\n",
" for i in input_nodes:\n",
" for j in output_nodes:\n",
" u.append(i)\n",
" v.append(j)\n",
" g.add_edges(u, v)\n",
" return g, input_nodes, output_nodes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2. Initialization & Affine Transformation\n",
"- Pre-compute $\\hat{u}_{j|i}$, initialize $b_{ij}$ and store them as edge attribute\n",
"- Initialize node features as zero\n",
"<img src=\"./capsule_f4.png\" style=\"height:200px;\"/>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# x is the input vextor with shape [batch_size, input_capsule_dim, input_num]\n",
"# Transpose x to [batch_size, input_num, input_capsule_dim] \n",
"x = x.transpose(1, 2)\n",
"# Expand x to [batch_size, input_num, output_num, input_capsule_dim, 1]\n",
"x = torch.stack([x] * self.output_capsule_num, dim=2).unsqueeze(4)\n",
"# Expand W from [input_num, output_num, input_capsule_dim, output_capsule_dim] \n",
"# to [batch_size, input_num, output_num, output_capsule_dim, input_capsule_dim] \n",
"W = self.weight.expand(self.batch_size, *self.weight.size())\n",
"# u_hat's shape is [input_num, output_num, batch_size, output_capsule_dim]\n",
"u_hat = torch.matmul(W, x).permute(1, 2, 0, 3, 4).squeeze().contiguous()\n",
"\n",
"b_ij = torch.zeros(self.input_capsule_num, self.output_capsule_num).to(self.device)\n",
"\n",
"self.g.set_e_repr({'b_ij': b_ij.view(-1)})\n",
"self.g.set_e_repr({'u_hat': u_hat.view(-1, self.batch_size, self.unit_size)})\n",
"\n",
"# Initialize all node features as zero\n",
"node_features = torch.zeros(self.input_capsule_num + self.output_capsule_num, self.batch_size,\n",
" self.output_capsule_dim).to(self.device)\n",
"self.g.set_n_repr({'h': node_features})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3. Write Message Passing functions and Squash function"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 3.1 Squash function\n",
"Squashing function is to ensure that short vectors get shrunk to almost zero length and long vectors get shrunk to a length slightly below 1.\n",
"![squash](./squash.png)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def squash(s):\n",
" msg_sq = torch.sum(s ** 2, dim=2, keepdim=True)\n",
" msg = torch.sqrt(msg_sq)\n",
" s = (msg_sq / (1.0 + msg_sq)) * (s / msg)\n",
" return s"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 3.2 Message Functions\n",
"At first stage, we need to define a message function to get all the attributes we need in the further computations."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def capsule_msg(src, edge):\n",
" return {'b_ij': edge['b_ij'], 'h': src['h'], 'u_hat': edge['u_hat']}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 3.3 Reduce Functions\n",
"At this stage, we need to define a reduce function to aggregate all the information we get from message function into node features.\n",
"This step implements the line 4 and line 5 in routing algorithms, which softmax over $b_{ij}$ and calculate weighted sum of input features. Note that softmax operation is over dimension $j$ instead of $i$. \n",
"<img src=\"./capsule_f5.png\" style=\"height:300px\">"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
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"source": [
"def capsule_reduce(node, msg):\n",
" b_ij_c, u_hat = msg['b_ij'], msg['u_hat']\n",
" # line 4\n",
" c_i = F.softmax(b_ij_c, dim=0)\n",
" # line 5\n",
" s_j = (c_i.unsqueeze(2).unsqueeze(3) * u_hat).sum(dim=1)\n",
" return {'h': s_j}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 3.4 Node Update Functions\n",
"Squash the intermidiate representations into node features $v_j$\n",
"![step6](./step6.png)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
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"source": [
"def capsule_update(msg):\n",
" # line 6\n",
" v_j = squash(msg['h'])\n",
" return {'h': v_j}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 3.5 Edge Update Functions\n",
"Update the routing parameters\n",
"![step7](./step7.png)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
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"def update_edge(self, u, v, edge):\n",
" return {'b_ij': edge['b_ij'] + (v['h'] * edge['u_hat']).mean(dim=1).sum(dim=1)}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4. Executing algorithm\n",
"Call `update_all` and `update_edge` functions to execute the algorithms"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
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"for i in range(self.num_routing):\n",
" self.g.update_all(self.capsule_msg, self.capsule_reduce, self.capsule_update)\n",
" self.g.update_edge(edge_func=self.update_edge)"
]
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{
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"cell_type": "code",
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"name": "stdout",
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"text": [
"env: DGL_LIBRARY_PATH=/data/jinjing/dgl/build\n",
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%env DGL_LIBRARY_PATH=/data/jinjing/dgl/build\n",
"%pylab inline\n",
"%config InlineBackend.figure_format = 'svg'\n",
"\n",
"import torch as th\n",
"import numpy as np\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"import dgl\n",
"import networkx as nx\n",
"\n",
"\n",
"def update_edge(u, v, edge):\n",
" return {'b_ij': edge['b_ij'] + (v['h'] * edge['u_hat']).sum(dim=1)}\n",
"\n",
"\n",
"def v2_reduce(node, msg):\n",
" b_ij_c, u_hat = msg['b_ij'], msg['u_hat']\n",
" c_i = F.softmax(b_ij_c, dim=0)\n",
" s_j = (c_i.unsqueeze(2) * u_hat).sum(dim=1)\n",
" return {'s_j': s_j}\n",
"\n",
"\n",
"def capsule_msg(src, edge):\n",
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"\n",
"\n",
"def squash(s):\n",
" s = s['s_j']\n",
" msg_sq = th.sum(s**2, dim=1, keepdim=True)\n",
" msg = th.sqrt(msg_sq)\n",
" s = (msg_sq / (1.0 + msg_sq)) * (s / msg)\n",
" return {'h': s}\n",
"\n",
"\n",
"np.random.seed(10)\n",
"num_points = 10\n",
"num_points2 = 2\n",
"\n",
"theta_dist1 = np.pi * (np.random.rand(num_points) * 2)\n",
"\n",
"radius_1 = np.random.rand(num_points) * 2 + 3\n",
"points_1 = th.from_numpy(\n",
" np.array([np.cos(theta_dist1) * radius_1,\n",
" np.sin(theta_dist1) * radius_1])).float()"
]
},
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"g = dgl.DGLGraph()\n",
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"\n",
"for i in range(num_points):\n",
" for j in range(num_points2):\n",
" g.add_edge(i, j + num_points)\n",
"\n",
"nodes1 = list(range(num_points))\n",
"nodes2 = list(range(num_points, num_points + num_points2))"
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"metadata": {},
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"u_hat = th.matmul(points_1.t().unsqueeze(1), W).squeeze(2).view(-1, 2)"
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"/home/jinjing/.pyenv/versions/3.6.1/lib/python3.6/site-packages/dgl-0.0.1-py3.6.egg/dgl/frame.py:256: UserWarning: Initializer is not set. Use zero initializer instead. To suppress this warning, use `set_initializer` to explicitly specify which initializer to use.\n",
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"source": [
"iterations=10\n",
"selected_node_feats=[points_1.data.numpy().T]\n",
"for i in range(iterations):\n",
" g.update_all(capsule_msg, v2_reduce, squash)\n",
" g.update_edge(edge_func=update_edge)\n",
" \n",
" new_=g.get_n_repr()['h'][num_points:num_points+num_points2]\n",
" np_new=new_.data.numpy()\n",
" if i in (1,3,7,9):\n",
" selected_node_feats.append(np_new)\n",
" plt.scatter(*np_new.T,color='blue')\n",
" plt.scatter(*points_1, color='red')\n",
" plt.title(f\"Routing Number: {i}\")\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
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"source": [
"for feats in selected_node_feats:\n",
" plt.scatter(*feats.T,color='blue')\n",
" plt.scatter(*points_1, color='red')\n",
" plt.scatter(*feats[0].T, color='aqua')\n",
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},
{
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tutorial/capsule/Untitled.ipynb deleted 100644 → 0
View file @ a50bbe58
{
"cells": [
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
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"text": [
"env: DGL_LIBRARY_PATH=/data/jinjing/dgl/build\n"
]
}
],
"source": [
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]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import torch as th\n",
"import numpy as np\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"num_points=10\n",
"num_points2=10"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"theta_dist1=np.pi*(np.random.rand(num_points)*2)\n",
"theta_dist2=np.pi*(np.random.rand(num_points2)*2)\n",
"\n",
"radius_1=np.random.rand(num_points)\n",
"radius_2=np.random.rand(num_points2)*2+3"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"points_1=np.array([np.cos(theta_dist1)*radius_1,np.sin(theta_dist1)*radius_1])"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"points_2=np.array([np.cos(theta_dist2)*radius_2,np.sin(theta_dist2)*radius_2])"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab inline"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [],
"source": [
"point_3=np.array([1,1])"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7fd47d377cf8>"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(*points_1,color='red')\n",
"plt.scatter(*points_2,color='blue')\n",
"plt.scatter(*point_3,color='aqua')"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"import torch as th\n",
"import dgl\n",
"import networkx as nx"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"g=dgl.DGLGraph()"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"g.add_nodes(num_points+num_points2)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"for i in range(num_points):\n",
" for j in range(num_points2):\n",
" g.add_edge(i,j+num_points)"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [],
"source": [
"nodes1=list(range(num_points))\n",
"nodes2=list(range(num_points,num_points+num_points2))"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [],
"source": [
"g.set_n_repr({'h':th.from_numpy(np.concatenate([points_1,points_2],axis=1).T)})"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {},
"outputs": [],
"source": [
"g.update_edge(edge_func=lambda u,v,edge:{'u_hat':u['h']})"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [],
"source": [
"g.set_e_repr({'b_ij':th.zeros(100)})\n",
"g.set_e_repr({'u_hat':})"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {},
"outputs": [],
"source": [
"def update_edge(self, u, v, edge):\n",
" return {'b_ij': edge['b_ij'] + (v['h'] * edge['u_hat']).mean(dim=1).sum(dim=1)}"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {},
"outputs": [],
"source": [
"def v2_reduce(node, msg):\n",
" b_ij_c, u_hat = msg['b_ij'], msg['u_hat']\n",
" c_i = F.softmax(b_ij_c, dim=0)\n",
" s_j = (c_i.unsqueeze(2).unsqueeze(3) * u_hat).sum(dim=1)\n",
" return {'h': s_j}"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {},
"outputs": [],
"source": [
"def capsule_msg(src, edge):\n",
" return {'b_ij': edge['b_ij'], 'h': src['h'], 'u_hat': edge['u_hat']}\n"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"20"
]
},
"execution_count": 124,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"g.number_of_nodes()"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"> \u001b[0;32m<ipython-input-120-f6b14912c0b0>\u001b[0m(4)\u001b[0;36mv2_reduce\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32m 1 \u001b[0;31m\u001b[0;32mdef\u001b[0m \u001b[0mv2_reduce\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 2 \u001b[0;31m \u001b[0mb_ij_c\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu_hat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'b_ij'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'u_hat'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 3 \u001b[0;31m \u001b[0mc_i\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msoftmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb_ij_c\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m----> 4 \u001b[0;31m \u001b[0ms_j\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mc_i\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mu_hat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 5 \u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'h'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0ms_j\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\n",
"ipdb> c_i.shape\n",
"torch.Size([10, 40])\n",
"ipdb> u_hat.shape\n",
"torch.Size([10, 40, 2])\n",
"ipdb> q\n"
]
},
{
"ename": "RuntimeError",
"evalue": "The size of tensor a (50) must match the size of tensor b (10) at non-singleton dimension 1",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-123-847b35d9c410>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'debug'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcapsule_msg\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mv2_reduce\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/.pyenv/versions/3.6.1/lib/python3.6/site-packages/dgl-0.0.1-py3.6.egg/dgl/graph.py\u001b[0m in \u001b[0;36mupdate_all\u001b[0;34m(self, message_func, reduce_func, apply_node_func)\u001b[0m\n\u001b[1;32m 1138\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1139\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mALL\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mALL\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage_func\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1140\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mALL\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduce_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1141\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_nodes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mALL\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mapply_node_func\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.pyenv/versions/3.6.1/lib/python3.6/site-packages/dgl-0.0.1-py3.6.egg/dgl/graph.py\u001b[0m in \u001b[0;36mrecv\u001b[0;34m(self, u, reduce_func, apply_node_func)\u001b[0m\n\u001b[1;32m 944\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreduce_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 945\u001b[0m \u001b[0mreduce_func\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBundledReduceFunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreduce_func\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 946\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_batch_recv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduce_func\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 947\u001b[0m \u001b[0;31m# optional apply nodes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 948\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_nodes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mapply_node_func\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.pyenv/versions/3.6.1/lib/python3.6/site-packages/dgl-0.0.1-py3.6.egg/dgl/graph.py\u001b[0m in \u001b[0;36m_batch_recv\u001b[0;34m(self, v, reduce_func)\u001b[0m\n\u001b[1;32m 990\u001b[0m lambda key: _reshape_fn(in_msgs[key]), self._msg_frame.schemes)\n\u001b[1;32m 991\u001b[0m \u001b[0mreordered_v\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv_bkt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtousertensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 992\u001b[0;31m \u001b[0mnew_reprs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreduce_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdst_reprs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreshaped_in_msgs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 993\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 994\u001b[0m \u001b[0;31m# TODO: clear partial messages\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-120-f6b14912c0b0>\u001b[0m in \u001b[0;36mv2_reduce\u001b[0;34m(node, msg)\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mb_ij_c\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu_hat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'b_ij'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'u_hat'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mc_i\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msoftmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb_ij_c\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0ms_j\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mc_i\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mu_hat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'h'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0ms_j\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mRuntimeError\u001b[0m: The size of tensor a (50) must match the size of tensor b (10) at non-singleton dimension 1"
]
}
],
"source": [
"%debug\n",
"g.update_all(capsule_msg,v2_reduce,lambda x:x)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"g.set_e_repr({'u_hat':th.zeros()})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"b_ij="
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"g.set_e_repr()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"nxg=g.to_networkx()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
tutorial/capsule/codes/CHANGELOG.md deleted 100644 → 0
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# Changelog
All notable changes to this project will be documented in this file.
The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/)
and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.html).
## [0.4.0] - 2018-01-30
### Added
- Supports and works with CIFAR10 dataset.
### Changed
- Upgrade to PyTorch 0.3.0.
- Supports CUDA 9.
- Drop our custom softmax function and switch to PyTorch softmax function.
- Modify the save_image utils function to handle 3-channel (RGB) image.
### Fixed
- Compatibilities with PyTorch 0.3.0.
## [0.3.0] - 2017-11-27
### Added
- Decoder network PyTorch module.
- Reconstruct image with Decoder network during testing.
- Save the original and recontructed images into file system.
- Log the original and reconstructed images using TensorBoard.
### Changed
- Refactor reconstruction loss function and decoder network.
- Remove image reconstruction from training.
## [0.2.0] - 2017-11-26
### Added
- New dependencies for TensorBoard and tqdm.
- Logging losses and accuracies with TensorBoard.
- New utils functions for:
- computing accuracy
- convert values of the model parameters to numpy.array.
- parsing boolean values with argparse
- Softmax function that takes a dimension.
- More detailed code comments.
- Show margin loss and reconstruction loss in logs.
- Show accuracy in train logs.
### Changed
- Refactor loss functions.
- Clean codes.
### Fixed
- Runtime error during pip install requirements.txt
- Bug in routing algorithm.
## [0.1.0] - 2017-11-12
### Added
- Implemented reconstruction loss.
- Saving reconstructed image as file.
- Improve training speed by using PyTorch DataParallel to wrap our model.
- PyTorch will parallelized the model and data over multiple GPUs.
- Supports training:
- on CPU (tested with macOS Sierra)
- on one GPU (tested with 1 Tesla K80 GPU)
- on multiple GPU (tested with 8 GPUs)
- with or without CUDA (tested with CUDA version 8.0.61)
- cuDNN 5 (tested with cuDNN 5.1.3)
### Changed
- More intuitive variable naming.
### Fixed
- Resolve Pylint warnings and reformat code.
- Missing square in equation 4 for margin (class) loss.
## 0.0.1 - 2017-11-04
### Added
- Initial release. The first beta version. API is stable. The code runs. So, I think it's safe to use for development but not ready for general production usage.
[Unreleased]: https://github.com/cedrickchee/capsule-net-pytorch/compare/v1.0.0...HEAD
[0.1.0]: https://github.com/cedrickchee/capsule-net-pytorch/compare/v0.0.1...v0.1.0
[0.2.0]: https://github.com/cedrickchee/capsule-net-pytorch/compare/v0.1.0...v0.2.0
[0.3.0]: https://github.com/cedrickchee/capsule-net-pytorch/compare/v0.2.0...v0.3.0
[0.4.0]: https://github.com/cedrickchee/capsule-net-pytorch/compare/v0.3.0...v0.4.0
tutorial/capsule/codes/LICENSE deleted 100644 → 0
View file @ a50bbe58
COPYRIGHT
All contributions by Cedric Chee:
Copyright (c) 2017, Cedric Chee.
All rights reserved.
All other contributions:
Copyright (c) 2017, the respective contributors.
All rights reserved.
Each contributor holds copyright over their respective contributions.
The project versioning (Git) records all such contribution source information.
LICENSE
The MIT License (MIT)
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
tutorial/capsule/codes/README.md deleted 100644 → 0
View file @ a50bbe58
# PyTorch CapsNet: Capsule Network for PyTorch
[![license](https://img.shields.io/github/license/mashape/apistatus.svg?maxAge=2592000)](https://github.com/cedrickchee/capsule-net-pytorch/blob/master/LICENSE)
![completion](https://img.shields.io/badge/completion%20state-95%25-green.svg?style=plastic)
A CUDA-enabled PyTorch implementation of CapsNet (Capsule Network) based on this paper:
[Sara Sabour, Nicholas Frosst, Geoffrey E Hinton. Dynamic Routing Between Capsules. NIPS 2017](https://arxiv.org/abs/1710.09829)
The current `test error is 0.21%` and the `best test error is 0.20%`. The current `test accuracy is 99.31%` and the `best test accuracy is 99.32%`.
**What is a Capsule**
> A Capsule is a group of neurons whose activity vector represents the instantiation parameters of a specific type of entity such as an object or object part.
You can learn more about Capsule Networks [here](#learning-resources).
**Why another CapsNet implementation?**
I wanted a decent PyTorch implementation of CapsNet and I couldn't find one at the point when I started. The goal of this implementation is focus to help newcomers learn and understand the CapsNet architecture and the idea of Capsules. The implementation is **NOT** focus on rigorous correctness of the results. In addition, the codes are not optimized for speed. To help us read and understand the codes easier, the codes comes with ample comments and the Python classes and functions are documented with Python docstring.
I will try my best to check and fix issues reported. Contributions are highly welcomed. If you find any bugs or errors in the codes, please do not hesitate to open an issue or a pull request. Thank you.
**Status and Latest Updates:**
See the [CHANGELOG](CHANGELOG.md)
**Datasets**
The model was trained on the standard [MNIST](http://yann.lecun.com/exdb/mnist/) data.
*Note: you don't have to manually download, preprocess, and load the MNIST dataset as [TorchVision](https://github.com/pytorch/vision) will take care of this step for you.*
I have tried using other datasets. See the [Other Datasets](#other-datasets) section below for more details.
## Requirements
- Python 3
- Tested with version 3.6.4
- [PyTorch](http://pytorch.org/)
- Tested with version 0.3.0.post4
- Migrate existing code to work in version 0.4.0. [Work-In-Progress]
- Code will not run with version 0.1.2 due to `keepdim` not available in this version.
- Code will not run with version 0.2.0 due to `softmax` function doesn't takes a dimension.
- CUDA 8 and above
- Tested with CUDA 8 and CUDA 9.
- [TorchVision](https://github.com/pytorch/vision)
- [tensorboardX](https://github.com/lanpa/tensorboard-pytorch)
- [tqdm](https://github.com/tqdm/tqdm)
## Usage
### Training and Evaluation
**Step 1.**
Clone this repository with ``git`` and install project dependencies.
```bash
$ git clone https://github.com/cedrickchee/capsule-net-pytorch.git
$ cd capsule-net-pytorch
$ pip install -r requirements.txt
```
**Step 2.**
Start the CapsNet on MNIST training and evaluation:
- Training with default settings:
```bash
$ python main.py
```
- Training on 8 GPUs with 30 epochs and 1 routing iteration:
```bash
$ CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python main.py --epochs 30 --num-routing 1 --threads 16 --batch-size 128 --test-batch-size 128
```
**Step 3.**
Test a pre-trained model:
If you have trained a model in Step 2 above, then the weights for the trained model will be saved to `results/trained_model/model_epoch_10.pth`. [WIP] Now just run the following command to get test results.
```bash
$ python main.py --is-training 0 --weights results/trained_model/model_epoch_10.pth
```
### Pre-trained Model
You can download the weights for the pre-trained model from my Google Drive. We saved the weights (model state dict) and the optimizer state for the model at the end of every training epoch.
- Weights from [epoch 50 checkpoint](https://drive.google.com/uc?export=download&id=1lYtOMSreP4I9hr9un4DsBJZrzodI6l2d) [84 MB].
- Weights from [epoch 40 to 50](https://drive.google.com/uc?export=download&id=1VMuVtJrecz47czsT5HqLxZpFjkLoMKaL) checkpoints [928 MB].
Uncompress and put the weights (.pth files) into `./results/trained_model/`.
*Note: the model was **last trained** on 2017-11-26 and the weights **last updated** on 2017-11-28.*
### The Default Hyper Parameters
| Parameter | Value | CLI arguments |
| --- | --- | --- |
| Training epochs | 10 | --epochs 10 |
| Learning rate | 0.01 | --lr 0.01 |
| Training batch size | 128 | --batch-size 128 |
| Testing batch size | 128 | --test-batch-size 128 |
| Log interval | 10 | --log-interval 10 |
| Disables CUDA training | false | --no-cuda |
| Num. of channels produced by the convolution | 256 | --num-conv-out-channel 256 |
| Num. of input channels to the convolution | 1 | --num-conv-in-channel 1 |
| Num. of primary unit | 8 | --num-primary-unit 8 |
| Primary unit size | 1152 | --primary-unit-size 1152 |
| Num. of digit classes | 10 | --num-classes 10 |
| Output unit size | 16 | --output-unit-size 16 |
| Num. routing iteration | 3 | --num-routing 3 |
| Use reconstruction loss | true | --use-reconstruction-loss |
| Regularization coefficient for reconstruction loss | 0.0005 | --regularization-scale 0.0005 |
| Dataset name (mnist, cifar10) | mnist | --dataset mnist |
| Input image width to the convolution | 28 | --input-width 28 |
| Input image height to the convolution | 28 | --input-height 28 |
## Results
### Test Error
CapsNet classification test error on MNIST. The MNIST average and standard deviation results are reported from 3 trials.
The results can be reproduced by running the following commands.
```bash
python main.py --epochs 50 --num-routing 1 --use-reconstruction-loss no --regularization-scale 0.0 #CapsNet-v1
python main.py --epochs 50 --num-routing 1 --use-reconstruction-loss yes --regularization-scale 0.0005 #CapsNet-v2
python main.py --epochs 50 --num-routing 3 --use-reconstruction-loss no --regularization-scale 0.0 #CapsNet-v3
python main.py --epochs 50 --num-routing 3 --use-reconstruction-loss yes --regularization-scale 0.0005 #CapsNet-v4
```
Method | Routing | Reconstruction | MNIST (%) | *Paper*
:---------|:------:|:---:|:----:|:----:
Baseline | -- | -- | -- | *0.39*
CapsNet-v1 | 1 | no | -- | *0.34 (0.032)*
CapsNet-v2 | 1 | yes | -- | *0.29 (0.011)*
CapsNet-v3 | 3 | no | -- | *0.35 (0.036)*
CapsNet-v4 | 3 | yes | 0.21 | *0.25 (0.005)*
### Training Loss and Accuracy
The training losses and accuracies for CapsNet-v4 (50 epochs, 3 routing iteration, using reconstruction, regularization scale of 0.0005):
![](results/train_loss_accuracy.png)
Training accuracy. Highest training accuracy: 100%
![](results/train_accuracy.png)
Training loss. Lowest training error: 0.1938%
![](results/train_loss.png)
### Test Loss and Accuracy
The test losses and accuracies for CapsNet-v4 (50 epochs, 3 routing iteration, using reconstruction, regularization scale of 0.0005):
![](results/test_loss_accuracy.png)
Test accuracy. Highest test accuracy: 99.32%
![](results/test_accuracy.png)
Test loss. Lowest test error: 0.2002%
![](results/test_loss.png)
### Training Speed
- Around `5.97s / batch` or `8min / epoch` on a single Tesla K80 GPU with batch size of 704.
- Around `3.25s / batch` or `25min / epoch` on a single Tesla K80 GPUwith batch size of 128.
![](results/training_speed.png)
In my case, these are the hyperparameters I used for the training setup:
- batch size: 128
- Epochs: 50
- Num. of routing: 3
- Use reconstruction loss: yes
- Regularization scale for reconstruction loss: 0.0005
### Reconstruction
The results of CapsNet-v4.
Digits at left are reconstructed images.
<table>
<tr>
<td>
<img src="results/reconstructed_images.png"/>
</td>
<td>
<p>[WIP] Ground truth image from dataset</p>
</td>
</tr>
</table>
### Model Design
```bash
Model architecture:
------------------
Net (
(conv1): ConvLayer (
(conv0): Conv2d(1, 256, kernel_size=(9, 9), stride=(1, 1))
(relu): ReLU (inplace)
)
(primary): CapsuleLayer (
(conv_units): ModuleList (
(0): Conv2d(256, 32, kernel_size=(9, 9), stride=(2, 2))
(1): Conv2d(256, 32, kernel_size=(9, 9), stride=(2, 2))
(2): Conv2d(256, 32, kernel_size=(9, 9), stride=(2, 2))
(3): Conv2d(256, 32, kernel_size=(9, 9), stride=(2, 2))
(4): Conv2d(256, 32, kernel_size=(9, 9), stride=(2, 2))
(5): Conv2d(256, 32, kernel_size=(9, 9), stride=(2, 2))
(6): Conv2d(256, 32, kernel_size=(9, 9), stride=(2, 2))
(7): Conv2d(256, 32, kernel_size=(9, 9), stride=(2, 2))
)
)
(digits): CapsuleLayer (
)
(decoder): Decoder (
(fc1): Linear (160 -> 512)
(fc2): Linear (512 -> 1024)
(fc3): Linear (1024 -> 784)
(relu): ReLU (inplace)
(sigmoid): Sigmoid ()
)
)
Parameters and size:
-------------------
conv1.conv0.weight: [256, 1, 9, 9]
conv1.conv0.bias: [256]
primary.conv_units.0.weight: [32, 256, 9, 9]
primary.conv_units.0.bias: [32]
primary.conv_units.1.weight: [32, 256, 9, 9]
primary.conv_units.1.bias: [32]
primary.conv_units.2.weight: [32, 256, 9, 9]
primary.conv_units.2.bias: [32]
primary.conv_units.3.weight: [32, 256, 9, 9]
primary.conv_units.3.bias: [32]
primary.conv_units.4.weight: [32, 256, 9, 9]
primary.conv_units.4.bias: [32]
primary.conv_units.5.weight: [32, 256, 9, 9]
primary.conv_units.5.bias: [32]
primary.conv_units.6.weight: [32, 256, 9, 9]
primary.conv_units.6.bias: [32]
primary.conv_units.7.weight: [32, 256, 9, 9]
primary.conv_units.7.bias: [32]
digits.weight: [1, 1152, 10, 16, 8]
decoder.fc1.weight: [512, 160]
decoder.fc1.bias: [512]
decoder.fc2.weight: [1024, 512]
decoder.fc2.bias: [1024]
decoder.fc3.weight: [784, 1024]
decoder.fc3.bias: [784]
Total number of parameters on (with reconstruction network): 8227088 (8 million)
```
### TensorBoard
We logged the training and test losses and accuracies using tensorboardX. TensorBoard helps us visualize how the machine learn over time. We can visualize statistics, such as how the objective function is changing or weights or accuracy varied during training.
TensorBoard operates by reading TensorFlow data (events files).
#### How to Use TensorBoard
1. Download a [copy of the events files](https://drive.google.com/uc?export=download&id=1lZVffeZTkUQfSxmZmYDViRzmhb59wBWL) for the latest run from my Google Drive.
2. Uncompress the file and put it into `./runs`.
3. Check to ensure you have installed tensorflow (CPU version). We need this for TensorBoard server and dashboard.
4. Start TensorBoard.
```bash
$ tensorboard --logdir runs
```
5. Open TensorBoard dashboard in your web browser using this URL: http://localhost:6006
### Other Datasets
#### CIFAR10
In the spirit of experiment, I have tried using other datasets. I have updated the implementation so that it supports and works with CIFAR10. Need to note that I have not tested throughly our capsule model on CIFAR10.
Here's how we can train and test the model on CIFAR10 by running the following commands.
```bash
python main.py --dataset cifar10 --num-conv-in-channel 3 --input-width 32 --input-height 32 --primary-unit-size 2048 --epochs 80 --num-routing 1 --use-reconstruction-loss yes --regularization-scale 0.0005
```
##### Training Loss and Accuracy
The training losses and accuracies for CapsNet-v4 (80 epochs, 3 routing iteration, using reconstruction, regularization scale of 0.0005):
![](results/cifar10/train_loss_accuracy.png)
- Highest training accuracy: 100%
- Lowest training error: 0.3589%
##### Test Loss and Accuracy
The test losses and accuracies for CapsNet-v4 (80 epochs, 3 routing iteration, using reconstruction, regularization scale of 0.0005):
![](results/cifar10/test_loss_accuracy.png)
- Highest test accuracy: 71%
- Lowest test error: 0.5735%
## TODO
- [x] Publish results.
- [x] More testing.
- [ ] Inference mode - command to test a pre-trained model.
- [ ] Jupyter Notebook version.
- [x] Create a sample to show how we can apply CapsNet to real-world application.
- [ ] Experiment with CapsNet:
* [x] Try using another dataset.
* [ ] Come out a more creative model structure.
- [x] Pre-trained model and weights.
- [x] Add visualization for training and evaluation metrics.
- [x] Implement recontruction loss.
- [x] Check algorithm for correctness.
- [x] Update results from TensorBoard after making improvements and bug fixes.
- [x] Publish updated pre-trained model weights.
- [x] Log the original and reconstructed images using TensorBoard.
- [ ] Update results with reconstructed image and original image.
- [ ] Resume training by loading model checkpoint.
- [ ] Migrate existing code to work in PyTorch 0.4.0.
*WIP is an acronym for Work-In-Progress*
## Credits
Referenced these implementations mainly for sanity check:
1. [TensorFlow implementation by @naturomics](https://github.com/naturomics/CapsNet-Tensorflow)
## Learning Resources
Here's some resources that we think will be helpful if you want to learn more about Capsule Networks:
- Articles and blog posts:
- [Understanding Hinton's Capsule Networks. Part I: Intuition.](https://medium.com/@pechyonkin/understanding-hintons-capsule-networks-part-i-intuition-b4b559d1159b)
- [Dynamic routing between capsules](https://blog.acolyer.org/2017/11/13/dynamic-routing-between-capsules/)
- [What is a CapsNet or Capsule Network?](https://hackernoon.com/what-is-a-capsnet-or-capsule-network-2bfbe48769cc)
- [Capsule Networks Are Shaking up AI — Here's How to Use Them](https://hackernoon.com/capsule-networks-are-shaking-up-ai-heres-how-to-use-them-c233a0971952)
- [Capsule Networks Explained](https://kndrck.co/posts/capsule_networks_explained/)
- Videos:
- [Capsule Networks: An Improvement to Convolutional Networks](https://www.youtube.com/watch?v=VKoLGnq15RM)
- [Capsule Networks (CapsNets) – Tutorial](https://www.youtube.com/watch?v=pPN8d0E3900)
## Other Implementations
- TensorFlow:
- The first author of the paper, [Sara Sabour has released the code](https://github.com/Sarasra/models/tree/master/research/capsules).
## Real-world Application of CapsNet
The following is a few samples in the wild that show how we can apply CapsNet to real-world use cases.
- [An attempt to implement CapsNet for car make-model classification](https://www.reddit.com/r/MachineLearning/comments/80eiz3/p_implementing_a_capsnet_for_car_makemodel/)
- [A Keras implementation of Capsule Network on Fashion MNIST dataset](https://github.com/XifengGuo/CapsNet-Fashion-MNIST)
\ No newline at end of file
tutorial/capsule/codes/capsule_layer.py deleted 100644 → 0
View file @ a50bbe58
"""Capsule layer
PyTorch implementation of CapsNet in Sabour, Hinton et al.'s paper
Dynamic Routing Between Capsules. NIPS 2017.
https://arxiv.org/abs/1710.09829
Author: Cedric Chee
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import utils
class CapsuleLayer(nn.Module):
"""
The core implementation of the idea of capsules
"""
def __init__(self, in_unit, in_channel, num_unit, unit_size, use_routing,
num_routing, cuda_enabled):
super(CapsuleLayer, self).__init__()
self.in_unit = in_unit
self.in_channel = in_channel
self.num_unit = num_unit
self.use_routing = use_routing
self.num_routing = num_routing
self.cuda_enabled = cuda_enabled
if self.use_routing:
"""
Based on the paper, DigitCaps which is capsule layer(s) with
capsule inputs use a routing algorithm that uses this weight matrix, Wij
"""
# weight shape:
# [1 x primary_unit_size x num_classes x output_unit_size x num_primary_unit]
# == [1 x 1152 x 10 x 16 x 8]
self.weight = nn.Parameter(torch.randn(1, in_channel, num_unit, unit_size, in_unit))
else:
"""
According to the CapsNet architecture section in the paper,
we have routing only between two consecutive capsule layers (e.g. PrimaryCapsules and DigitCaps).
No routing is used between Conv1 and PrimaryCapsules.
This means PrimaryCapsules is composed of several convolutional units.
"""
# Define 8 convolutional units.
self.conv_units = nn.ModuleList([
nn.Conv2d(self.in_channel, 32, 9, 2) for u in range(self.num_unit)
])
def forward(self, x):
if self.use_routing:
# Currently used by DigitCaps layer.
return self.routing(x)
else:
# Currently used by PrimaryCaps layer.
return self.no_routing(x)
def routing(self, x):
"""
Routing algorithm for capsule.
:input: tensor x of shape [128, 8, 1152]
:return: vector output of capsule j
"""
batch_size = x.size(0)
x = x.transpose(1, 2) # dim 1 and dim 2 are swapped. out tensor shape: [128, 1152, 8]
# Stacking and adding a dimension to a tensor.
# stack ops output shape: [128, 1152, 10, 8]
# unsqueeze ops output shape: [128, 1152, 10, 8, 1]
x = torch.stack([x] * self.num_unit, dim=2).unsqueeze(4)
# Convert single weight to batch weight.
# [1 x 1152 x 10 x 16 x 8] to: [128, 1152, 10, 16, 8]
batch_weight = torch.cat([self.weight] * batch_size, dim=0)
# u_hat is "prediction vectors" from the capsules in the layer below.
# Transform inputs by weight matrix.
# Matrix product of 2 tensors with shape: [128, 1152, 10, 16, 8] x [128, 1152, 10, 8, 1]
# u_hat shape: [128, 1152, 10, 16, 1]
u_hat = torch.matmul(batch_weight, x)
# All the routing logits (b_ij in the paper) are initialized to zero.
# self.in_channel = primary_unit_size = 32 * 6 * 6 = 1152
# self.num_unit = num_classes = 10
# b_ij shape: [1, 1152, 10, 1]
b_ij = Variable(torch.zeros(1, self.in_channel, self.num_unit, 1))
if self.cuda_enabled:
b_ij = b_ij.cuda()
# From the paper in the "Capsules on MNIST" section,
# the sample MNIST test reconstructions of a CapsNet with 3 routing iterations.
num_iterations = self.num_routing
for iteration in range(num_iterations):
# Routing algorithm
# Calculate routing or also known as coupling coefficients (c_ij).
# c_ij shape: [1, 1152, 10, 1]
c_ij = F.softmax(b_ij, dim=2) # Convert routing logits (b_ij) to softmax.
# c_ij shape from: [128, 1152, 10, 1] to: [128, 1152, 10, 1, 1]
c_ij = torch.cat([c_ij] * batch_size, dim=0).unsqueeze(4)
# Implement equation 2 in the paper.
# s_j is total input to a capsule, is a weigthed sum over all "prediction vectors".
# u_hat is weighted inputs, prediction ˆuj|i made by capsule i.
# c_ij * u_hat shape: [128, 1152, 10, 16, 1]
# s_j output shape: [batch_size=128, 1, 10, 16, 1]
# Sum of Primary Capsules outputs, 1152D becomes 1D.
s_j = (c_ij * u_hat).sum(dim=1, keepdim=True)
# Squash the vector output of capsule j.
# v_j shape: [batch_size, weighted sum of PrimaryCaps output,
# num_classes, output_unit_size from u_hat, 1]
# == [128, 1, 10, 16, 1]
# So, the length of the output vector of a capsule is 16, which is in dim 3.
v_j = utils.squash(s_j, dim=3)
# in_channel is 1152.
# v_j1 shape: [128, 1152, 10, 16, 1]
v_j1 = torch.cat([v_j] * self.in_channel, dim=1)
# The agreement.
# Transpose u_hat with shape [128, 1152, 10, 16, 1] to [128, 1152, 10, 1, 16],
# so we can do matrix product u_hat and v_j1.
# u_vj1 shape: [1, 1152, 10, 1]
u_vj1 = torch.matmul(u_hat.transpose(3, 4), v_j1).squeeze(4).mean(dim=0, keepdim=True)
# Update routing (b_ij) by adding the agreement to the initial logit.
b_ij = b_ij + u_vj1
return v_j.squeeze(1) # shape: [128, 10, 16, 1]
def no_routing(self, x):
"""
Get output for each unit.
A unit has batch, channels, height, width.
An example of a unit output shape is [128, 32, 6, 6]
:return: vector output of capsule j
"""
# Create 8 convolutional unit.
# A convolutional unit uses normal convolutional layer with a non-linearity (squash).
unit = [self.conv_units[i](x) for i, l in enumerate(self.conv_units)]
# Stack all unit outputs.
# Stacked of 8 unit output shape: [128, 8, 32, 6, 6]
unit = torch.stack(unit, dim=1)
batch_size = x.size(0)
# Flatten the 32 of 6x6 grid into 1152.
# Shape: [128, 8, 1152]
unit = unit.view(batch_size, self.num_unit, -1)
# Add non-linearity
# Return squashed outputs of shape: [128, 8, 1152]
return utils.squash(unit, dim=2) # dim 2 is the third dim (1152D array) in our tensor
tutorial/capsule/codes/conv_layer.py deleted 100644 → 0
View file @ a50bbe58
"""Convolutional layer
PyTorch implementation of CapsNet in Sabour, Hinton et al.'s paper
Dynamic Routing Between Capsules. NIPS 2017.
https://arxiv.org/abs/1710.09829
Author: Cedric Chee
"""
import torch
import torch.nn as nn
class ConvLayer(nn.Module):
"""
Conventional Conv2d layer
"""
def __init__(self, in_channel, out_channel, kernel_size):
super(ConvLayer, self).__init__()
self.conv0 = nn.Conv2d(in_channels=in_channel,
out_channels=out_channel,
kernel_size=kernel_size,
stride=1)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
"""Forward pass"""
# x shape: [128, 1, 28, 28]
# out_conv0 shape: [128, 256, 20, 20]
out_conv0 = self.conv0(x)
# out_relu shape: [128, 256, 20, 20]
out_relu = self.relu(out_conv0)
return out_relu
tutorial/capsule/codes/decoder.py deleted 100644 → 0
View file @ a50bbe58
"""Decoder Network
PyTorch implementation of CapsNet in Sabour, Hinton et al.'s paper
Dynamic Routing Between Capsules. NIPS 2017.
https://arxiv.org/abs/1710.09829
Author: Cedric Chee
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import utils
class Decoder(nn.Module):
"""
Implement Decoder structure in section 4.1, Figure 2 to reconstruct a digit
from the `DigitCaps` layer representation.
The decoder network consists of 3 fully connected layers. For each
[10, 16] output, we mask out the incorrect predictions, and send
the [16,] vector to the decoder network to reconstruct a [784,] size
image.
This Decoder network is used in training and prediction (testing).
"""
def __init__(self, num_classes, output_unit_size, input_width,
input_height, num_conv_in_channel, cuda_enabled):
"""
The decoder network consists of 3 fully connected layers, with
512, 1024, 784 (or 3072 for CIFAR10) neurons each.
"""
super(Decoder, self).__init__()
self.cuda_enabled = cuda_enabled
fc1_output_size = 512
fc2_output_size = 1024
self.fc3_output_size = input_width * input_height * num_conv_in_channel
self.fc1 = nn.Linear(num_classes * output_unit_size, fc1_output_size) # input dim 10 * 16.
self.fc2 = nn.Linear(fc1_output_size, fc2_output_size)
self.fc3 = nn.Linear(fc2_output_size, self.fc3_output_size)
# Activation functions
self.relu = nn.ReLU(inplace=True)
self.sigmoid = nn.Sigmoid()
def forward(self, x, target):
"""
We send the outputs of the `DigitCaps` layer, which is a
[batch_size, 10, 16] size tensor into the Decoder network, and
reconstruct a [batch_size, fc3_output_size] size tensor representing the image.
Args:
x: [batch_size, 10, 16] The output of the digit capsule.
target: [batch_size, 10] One-hot MNIST dataset labels.
Returns:
reconstruction: [batch_size, fc3_output_size] Tensor of reconstructed images.
"""
batch_size = target.size(0)
"""
First, do masking.
"""
# Method 1: mask with y.
# Note: we have not implement method 2 which is masking with true label.
# masked_caps shape: [batch_size, 10, 16, 1]
masked_caps = utils.mask(x, self.cuda_enabled)
"""
Second, reconstruct the images with 3 Fully Connected layers.
"""
# vector_j shape: [batch_size, 160=10*16]
vector_j = masked_caps.view(x.size(0), -1) # reshape the masked_caps tensor
# Forward pass of the network
fc1_out = self.relu(self.fc1(vector_j))
fc2_out = self.relu(self.fc2(fc1_out)) # shape: [batch_size, 1024]
reconstruction = self.sigmoid(self.fc3(fc2_out)) # shape: [batch_size, fc3_output_size]
assert reconstruction.size() == torch.Size([batch_size, self.fc3_output_size])
return reconstruction
tutorial/capsule/codes/dgl_capsule_batch.py deleted 100644 → 0
View file @ a50bbe58
import dgl
import torch
import torch.nn.functional as F
from torch import nn
from capsule_layer import CapsuleLayer
# import main
from utils import writer, step
# global_step = main.global_step
device = "cuda" if torch.cuda.is_available() else "cpu"
class DGLFeature():
"""
To wrap different shape of representation tensor into the same shape
"""
def __init__(self, tensor, pad_to):
# self.tensor = tensor
self.node_num = tensor.size(0)
self.flat_tensor = tensor.contiguous().view(self.node_num, -1)
self.node_feature_dim = self.flat_tensor.size(1)
self.flat_pad_tensor = F.pad(self.flat_tensor, (0, pad_to - self.flat_tensor.size(1)))
self.shape = tensor.shape
@property
def tensor(self):
"""
:return: Tensor with original shape
"""
return self.flat_tensor.index_select(1, torch.arange(0, self.node_feature_dim).to(device)).view(self.shape)
@property
def padded_tensor(self):
"""
:return: Flatted and padded Tensor
"""
return self.flat_pad_tensor
class DGLBatchCapsuleLayer(CapsuleLayer):
def __init__(self, in_unit, in_channel, num_unit, unit_size, use_routing,
num_routing, cuda_enabled):
super(DGLBatchCapsuleLayer, self).__init__(in_unit, in_channel, num_unit, unit_size, use_routing,
num_routing, cuda_enabled)
self.unit_size = unit_size
self.weight = nn.Parameter(torch.randn(in_channel, num_unit, unit_size, in_unit))
def routing(self, x):
self.batch_size = x.size(0)
self.g = dgl.DGLGraph()
self.g.add_nodes_from([i for i in range(self.in_channel)])
self.g.add_nodes_from([i + self.in_channel for i in range(self.num_unit)])
for i in range(self.in_channel):
for j in range(self.num_unit):
index_j = j + self.in_channel
self.g.add_edge(i, index_j)
self.edge_features = torch.zeros(self.in_channel, self.num_unit).to('cuda')
x_ = x.transpose(0, 2)
x_ = DGLFeature(x_, self.batch_size * self.unit_size)
x = x.transpose(1, 2)
x = torch.stack([x] * self.num_unit, dim=2).unsqueeze(4)
W = torch.cat([self.weight.unsqueeze(0)] * self.batch_size, dim=0)
u_hat = torch.matmul(W, x).permute(1, 2, 0, 3, 4).squeeze().contiguous()
self.node_feature = DGLFeature(torch.zeros(self.num_unit, self.batch_size, self.unit_size).to('cuda'),
self.batch_size * self.unit_size)
nf = torch.cat([x_.padded_tensor, self.node_feature.padded_tensor], dim=0)
self.g.set_e_repr({'b_ij': self.edge_features.view(-1)})
self.g.set_n_repr({'h': nf})
self.g.set_e_repr({'u_hat': u_hat.view(-1, self.batch_size, self.unit_size)})
for i in range(self.num_routing):
self.i = i
self.g.update_all(self.capsule_msg, self.capsule_reduce,
lambda x: {'h': DGLFeature(x['h'], self.batch_size * self.unit_size).padded_tensor},
batchable=True)
self.g.update_edge(dgl.base.ALL, dgl.base.ALL, self.update_edge, batchable=True)
self.node_feature = self.g.get_n_repr()['h'] \
.index_select(0, torch.arange(self.in_channel, self.in_channel + self.num_unit).to(device)) \
.view(self.num_unit, self.batch_size, self.unit_size)
return self.node_feature.transpose(0, 1).unsqueeze(1).unsqueeze(4).squeeze(1)
def update_edge(self, u, v, edge):
return {
'b_ij': edge['b_ij'] + (v['h'].view(-1, self.batch_size, self.unit_size) * edge['u_hat']).mean(dim=1).sum(
dim=1)}
@staticmethod
def capsule_msg(src, edge):
return {'b_ij': edge['b_ij'], 'h': src['h'], 'u_hat': edge['u_hat']}
def capsule_reduce(self, node, msg):
b_ij_c, h_c, u_hat_c = msg['b_ij'], msg['h'], msg['u_hat']
u_hat = u_hat_c
c_i = F.softmax(b_ij_c, dim=0)
writer.add_histogram(f"c_i{self.i}", c_i, step['step'])
s_j = (c_i.unsqueeze(2).unsqueeze(3) * u_hat).sum(dim=1)
v_j = self.squash(s_j)
return {'h': v_j.view(-1, self.batch_size * self.unit_size)}
@staticmethod
def squash(s):
# This is equation 1 from the paper.
mag_sq = torch.sum(s ** 2, dim=2, keepdim=True)
mag = torch.sqrt(mag_sq)
s = (mag_sq / (1.0 + mag_sq)) * (s / mag)
return s
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